Overview

Dataset statistics

Number of variables23
Number of observations5250
Missing cells9646
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory907.6 KiB
Average record size in memory177.0 B

Variable types

Numeric6
Text6
Categorical5
Boolean6

Alerts

Modified_Operating_Hours_ has constant value ""Constant
Fuel_Station_open_to_public_ has constant value ""Constant
businessunit_number is highly overall correlated with businessunit_status_code and 2 other fieldsHigh correlation
businessunit_status_code is highly overall correlated with businessunit_number and 2 other fieldsHigh correlation
bu_num is highly overall correlated with businessunit_number and 2 other fieldsHigh correlation
businessunit_banner_description is highly overall correlated with businessunit_type_descriptionHigh correlation
businessunit_type_description is highly overall correlated with businessunit_number and 2 other fieldsHigh correlation
businessunit_isstoreopen is highly overall correlated with businessunit_status_codeHigh correlation
Pharmacy_open_to_public_ is highly overall correlated with Grocery_delivery_status and 1 other fieldsHigh correlation
Grocery_delivery_status is highly overall correlated with Pharmacy_open_to_public_ and 1 other fieldsHigh correlation
Online_grocery_pickup_status is highly overall correlated with Pharmacy_open_to_public_ and 1 other fieldsHigh correlation
businessunit_banner_description is highly imbalanced (52.3%)Imbalance
businessunit_isstoreopen is highly imbalanced (96.2%)Imbalance
op_status is highly imbalanced (99.3%)Imbalance
Pharmacy_open_to_public_ is highly imbalanced (99.7%)Imbalance
Modified_Operating_Hours_ has 5247 (99.9%) missing valuesMissing
Grocery_delivery_service has 435 (8.3%) missing valuesMissing
Grocery_delivery_status has 2385 (45.4%) missing valuesMissing
Online_grocery_pickup has 106 (2.0%) missing valuesMissing
Online_grocery_pickup_status has 1473 (28.1%) missing valuesMissing
objectid has unique valuesUnique
businessunit_number has unique valuesUnique
bu_num has unique valuesUnique

Reproduction

Analysis started2023-10-22 10:05:59.441634
Analysis finished2023-10-22 10:06:09.218187
Duration9.78 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

X
Real number (ℝ)

Distinct5248
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.520243
Minimum-159.36503
Maximum-65.674303
Zeros0
Zeros (%)0.0%
Negative5250
Negative (%)100.0%
Memory size41.1 KiB
2023-10-22T12:06:09.355650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-159.36503
5-th percentile-119.18436
Q1-97.132078
median-88.420169
Q3-81.753295
95-th percentile-74.692686
Maximum-65.674303
Range93.690722
Interquartile range (IQR)15.378783

Descriptive statistics

Standard deviation13.529964
Coefficient of variation (CV)-0.14783575
Kurtosis1.3174184
Mean-91.520243
Median Absolute Deviation (MAD)7.550761
Skewness-1.0579582
Sum-480481.28
Variance183.05993
MonotonicityNot monotonic
2023-10-22T12:06:09.625258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-84.848736 2
 
< 0.1%
-90.324403 2
 
< 0.1%
-94.149054 1
 
< 0.1%
-117.266426 1
 
< 0.1%
-83.470749 1
 
< 0.1%
-75.091761 1
 
< 0.1%
-120.609275 1
 
< 0.1%
-85.663857 1
 
< 0.1%
-96.856976 1
 
< 0.1%
-76.928412 1
 
< 0.1%
Other values (5238) 5238
99.8%
ValueCountFrequency (%)
-159.365025 1
< 0.1%
-158.075334 1
< 0.1%
-158.034591 1
< 0.1%
-158.005571 1
< 0.1%
-157.978362 1
< 0.1%
-157.974452 1
< 0.1%
-157.862004 1
< 0.1%
-157.843233 1
< 0.1%
-157.842705 1
< 0.1%
-156.454892 1
< 0.1%
ValueCountFrequency (%)
-65.674303 1
< 0.1%
-65.807268 1
< 0.1%
-65.889641 1
< 0.1%
-65.995746 1
< 0.1%
-65.997031 1
< 0.1%
-66.015882 1
< 0.1%
-66.020899 1
< 0.1%
-66.076049 1
< 0.1%
-66.087612 1
< 0.1%
-66.095026 1
< 0.1%

Y
Real number (ℝ)

Distinct5247
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.45154
Minimum17.980621
Maximum64.856378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:10.042721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum17.980621
5-th percentile28.029205
Q132.959555
median36.181191
Q340.369625
95-th percentile44.552793
Maximum64.856378
Range46.875757
Interquartile range (IQR)7.4100702

Descriptive statistics

Standard deviation5.2184587
Coefficient of variation (CV)0.14316154
Kurtosis0.55972775
Mean36.45154
Median Absolute Deviation (MAD)3.6537935
Skewness-0.052431905
Sum191370.58
Variance27.232311
MonotonicityNot monotonic
2023-10-22T12:06:10.332205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.473938 2
 
< 0.1%
29.470675 2
 
< 0.1%
42.999723 2
 
< 0.1%
47.658489 1
 
< 0.1%
39.999062 1
 
< 0.1%
37.340296 1
 
< 0.1%
38.088633 1
 
< 0.1%
32.862217 1
 
< 0.1%
40.224885 1
 
< 0.1%
33.069254 1
 
< 0.1%
Other values (5237) 5237
99.8%
ValueCountFrequency (%)
17.980621 1
< 0.1%
17.993681 1
< 0.1%
17.99736 1
< 0.1%
18.016813 1
< 0.1%
18.044336 1
< 0.1%
18.122626 1
< 0.1%
18.141237 1
< 0.1%
18.155224 1
< 0.1%
18.243301 1
< 0.1%
18.246762 1
< 0.1%
ValueCountFrequency (%)
64.856378 1
< 0.1%
61.568719 1
< 0.1%
61.309037 1
< 0.1%
61.211988 1
< 0.1%
61.192239 1
< 0.1%
61.140195 1
< 0.1%
60.564278 1
< 0.1%
57.811934 1
< 0.1%
55.375474 1
< 0.1%
48.818547 1
< 0.1%

objectid
Real number (ℝ)

UNIQUE 

Distinct5250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32402.935
Minimum29040
Maximum36706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:10.578344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum29040
5-th percentile29377.45
Q130471.25
median32128.5
Q334228.75
95-th percentile36274.55
Maximum36706
Range7666
Interquartile range (IQR)3757.5

Descriptive statistics

Standard deviation2173.7084
Coefficient of variation (CV)0.067083689
Kurtosis-1.0958443
Mean32402.935
Median Absolute Deviation (MAD)1873.5
Skewness0.27415283
Sum1.7011541 × 108
Variance4725008.1
MonotonicityStrictly increasing
2023-10-22T12:06:10.893148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29040 1
 
< 0.1%
33536 1
 
< 0.1%
33533 1
 
< 0.1%
33532 1
 
< 0.1%
33531 1
 
< 0.1%
33530 1
 
< 0.1%
33529 1
 
< 0.1%
33527 1
 
< 0.1%
33525 1
 
< 0.1%
33524 1
 
< 0.1%
Other values (5240) 5240
99.8%
ValueCountFrequency (%)
29040 1
< 0.1%
29041 1
< 0.1%
29042 1
< 0.1%
29043 1
< 0.1%
29044 1
< 0.1%
29045 1
< 0.1%
29046 1
< 0.1%
29047 1
< 0.1%
29048 1
< 0.1%
29049 1
< 0.1%
ValueCountFrequency (%)
36706 1
< 0.1%
36705 1
< 0.1%
36704 1
< 0.1%
36702 1
< 0.1%
36700 1
< 0.1%
36698 1
< 0.1%
36695 1
< 0.1%
36693 1
< 0.1%
36690 1
< 0.1%
36688 1
< 0.1%
Distinct4795
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:11.303940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length42
Median length36
Mean length13.968762
Min length5

Characters and Unicode

Total characters73336
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4435 ?
Unique (%)84.5%

Sample

1st rowROGERS, AR
2nd rowTUNKHANNOCK, PA
3rd rowTAHLEQUAH OK
4th rowTRACY, CA
5th rowBENTONVILLE, AR
ValueCountFrequency (%)
tx 585
 
4.4%
fl 371
 
2.8%
ca 306
 
2.3%
ga 211
 
1.6%
nc 210
 
1.6%
il 180
 
1.4%
oh 167
 
1.3%
city 163
 
1.2%
la 158
 
1.2%
pa 157
 
1.2%
Other values (3243) 10690
81.0%
2023-10-22T12:06:12.185746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7957
 
10.9%
A 6356
 
8.7%
N 5106
 
7.0%
E 5093
 
6.9%
L 4848
 
6.6%
O 4608
 
6.3%
I 3791
 
5.2%
R 3712
 
5.1%
T 3649
 
5.0%
S 3397
 
4.6%
Other values (60) 24819
33.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60212
82.1%
Space Separator 7957
 
10.9%
Other Punctuation 2595
 
3.5%
Lowercase Letter 908
 
1.2%
Open Punctuation 678
 
0.9%
Close Punctuation 678
 
0.9%
Decimal Number 241
 
0.3%
Dash Punctuation 65
 
0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6356
 
10.6%
N 5106
 
8.5%
E 5093
 
8.5%
L 4848
 
8.1%
O 4608
 
7.7%
I 3791
 
6.3%
R 3712
 
6.2%
T 3649
 
6.1%
S 3397
 
5.6%
C 2561
 
4.3%
Other values (16) 17091
28.4%
Lowercase Letter
ValueCountFrequency (%)
a 111
12.2%
e 92
10.1%
o 91
10.0%
n 80
8.8%
l 76
 
8.4%
r 74
 
8.1%
i 55
 
6.1%
s 54
 
5.9%
t 52
 
5.7%
d 33
 
3.6%
Other values (14) 190
20.9%
Decimal Number
ValueCountFrequency (%)
1 83
34.4%
2 42
17.4%
0 37
15.4%
6 15
 
6.2%
7 13
 
5.4%
3 13
 
5.4%
4 13
 
5.4%
5 11
 
4.6%
8 9
 
3.7%
9 5
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 2422
93.3%
. 86
 
3.3%
/ 42
 
1.6%
& 38
 
1.5%
' 7
 
0.3%
Space Separator
ValueCountFrequency (%)
7957
100.0%
Open Punctuation
ValueCountFrequency (%)
( 678
100.0%
Close Punctuation
ValueCountFrequency (%)
) 678
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61120
83.3%
Common 12216
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6356
 
10.4%
N 5106
 
8.4%
E 5093
 
8.3%
L 4848
 
7.9%
O 4608
 
7.5%
I 3791
 
6.2%
R 3712
 
6.1%
T 3649
 
6.0%
S 3397
 
5.6%
C 2561
 
4.2%
Other values (40) 17999
29.4%
Common
ValueCountFrequency (%)
7957
65.1%
, 2422
 
19.8%
( 678
 
5.6%
) 678
 
5.6%
. 86
 
0.7%
1 83
 
0.7%
- 65
 
0.5%
/ 42
 
0.3%
2 42
 
0.3%
& 38
 
0.3%
Other values (10) 125
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7957
 
10.9%
A 6356
 
8.7%
N 5106
 
7.0%
E 5093
 
6.9%
L 4848
 
6.6%
O 4608
 
6.3%
I 3791
 
5.2%
R 3712
 
5.1%
T 3649
 
5.0%
S 3397
 
4.6%
Other values (60) 24819
33.8%

businessunit_number
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3184.756
Minimum1
Maximum9894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:12.656512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile278.45
Q11371.25
median2810.5
Q34925.75
95-th percentile6955.75
Maximum9894
Range9893
Interquartile range (IQR)3554.5

Descriptive statistics

Standard deviation2159.4932
Coefficient of variation (CV)0.6780718
Kurtosis-0.73966895
Mean3184.756
Median Absolute Deviation (MAD)1694
Skewness0.47704628
Sum16719969
Variance4663411
MonotonicityNot monotonic
2023-10-22T12:06:13.119211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5894 1
 
< 0.1%
5891 1
 
< 0.1%
5890 1
 
< 0.1%
589 1
 
< 0.1%
5889 1
 
< 0.1%
5888 1
 
< 0.1%
5886 1
 
< 0.1%
5884 1
 
< 0.1%
5883 1
 
< 0.1%
Other values (5240) 5240
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
9894 1
< 0.1%
8958 1
< 0.1%
8930 1
< 0.1%
8331 1
< 0.1%
8299 1
< 0.1%
8298 1
< 0.1%
8297 1
< 0.1%
8296 1
< 0.1%
8295 1
< 0.1%
8294 1
< 0.1%

businessunit_banner_description
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
WM Supercenter
3575 
Neighborhood Market
683 
Sam's Club
602 
Wal-Mart
364 
WM On Campus/RX Facilities
 
16
Other values (3)
 
10

Length

Max length26
Median length14
Mean length13.824381
Min length8

Characters and Unicode

Total characters72578
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowWM Supercenter
2nd rowWM Supercenter
3rd rowWM Supercenter
4th rowWM Supercenter
5th rowWM Supercenter

Common Values

ValueCountFrequency (%)
WM Supercenter 3575
68.1%
Neighborhood Market 683
 
13.0%
Sam's Club 602
 
11.5%
Wal-Mart 364
 
6.9%
WM On Campus/RX Facilities 16
 
0.3%
Walmart Fuel Station 8
 
0.2%
WM ONLINE PICKUP/DELIVERY 1
 
< 0.1%
STAND ALONE PICKUP 1
 
< 0.1%

Length

2023-10-22T12:06:13.553742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:13.953895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
wm 3592
35.3%
supercenter 3575
35.1%
neighborhood 683
 
6.7%
market 683
 
6.7%
sam's 602
 
5.9%
club 602
 
5.9%
wal-mart 364
 
3.6%
campus/rx 16
 
0.2%
facilities 16
 
0.2%
on 16
 
0.2%
Other values (8) 29
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 12115
16.7%
r 8888
12.2%
4928
 
6.8%
t 4662
 
6.4%
M 4639
 
6.4%
u 4201
 
5.8%
S 4186
 
5.8%
W 3964
 
5.5%
n 3599
 
5.0%
p 3591
 
4.9%
Other values (32) 17805
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52470
72.3%
Uppercase Letter 14197
 
19.6%
Space Separator 4928
 
6.8%
Other Punctuation 619
 
0.9%
Dash Punctuation 364
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 4639
32.7%
S 4186
29.5%
W 3964
27.9%
N 687
 
4.8%
C 620
 
4.4%
F 24
 
0.2%
O 18
 
0.1%
R 17
 
0.1%
X 16
 
0.1%
P 4
 
< 0.1%
Other values (10) 22
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 12115
23.1%
r 8888
16.9%
t 4662
 
8.9%
u 4201
 
8.0%
n 3599
 
6.9%
p 3591
 
6.8%
c 3591
 
6.8%
a 2069
 
3.9%
o 2057
 
3.9%
h 1366
 
2.6%
Other values (8) 6331
12.1%
Other Punctuation
ValueCountFrequency (%)
' 602
97.3%
/ 17
 
2.7%
Space Separator
ValueCountFrequency (%)
4928
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66667
91.9%
Common 5911
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12115
18.2%
r 8888
13.3%
t 4662
 
7.0%
M 4639
 
7.0%
u 4201
 
6.3%
S 4186
 
6.3%
W 3964
 
5.9%
n 3599
 
5.4%
p 3591
 
5.4%
c 3591
 
5.4%
Other values (28) 13231
19.8%
Common
ValueCountFrequency (%)
4928
83.4%
' 602
 
10.2%
- 364
 
6.2%
/ 17
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12115
16.7%
r 8888
12.2%
4928
 
6.8%
t 4662
 
6.4%
M 4639
 
6.4%
u 4201
 
5.8%
S 4186
 
5.8%
W 3964
 
5.5%
n 3599
 
5.0%
p 3591
 
4.9%
Other values (32) 17805
24.5%

businessunit_type_description
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
Retail
4648 
Wholesale
602 

Length

Max length9
Median length6
Mean length6.344
Min length6

Characters and Unicode

Total characters33306
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRetail
2nd rowRetail
3rd rowRetail
4th rowRetail
5th rowRetail

Common Values

ValueCountFrequency (%)
Retail 4648
88.5%
Wholesale 602
 
11.5%

Length

2023-10-22T12:06:14.377367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:14.642355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
retail 4648
88.5%
wholesale 602
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 5852
17.6%
l 5852
17.6%
a 5250
15.8%
R 4648
14.0%
t 4648
14.0%
i 4648
14.0%
W 602
 
1.8%
h 602
 
1.8%
o 602
 
1.8%
s 602
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28056
84.2%
Uppercase Letter 5250
 
15.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5852
20.9%
l 5852
20.9%
a 5250
18.7%
t 4648
16.6%
i 4648
16.6%
h 602
 
2.1%
o 602
 
2.1%
s 602
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
R 4648
88.5%
W 602
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 33306
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5852
17.6%
l 5852
17.6%
a 5250
15.8%
R 4648
14.0%
t 4648
14.0%
i 4648
14.0%
W 602
 
1.8%
h 602
 
1.8%
o 602
 
1.8%
s 602
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5852
17.6%
l 5852
17.6%
a 5250
15.8%
R 4648
14.0%
t 4648
14.0%
i 4648
14.0%
W 602
 
1.8%
h 602
 
1.8%
o 602
 
1.8%
s 602
 
1.8%

businessunit_isstoreopen
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
True
5229 
False
 
21
ValueCountFrequency (%)
True 5229
99.6%
False 21
 
0.4%
2023-10-22T12:06:14.892349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct5241
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:15.371484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length41
Median length35
Mean length17.665524
Min length8

Characters and Unicode

Total characters92744
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5234 ?
Unique (%)99.7%

Sample

1st row2110 W WALNUT ST
2nd row808 HUNTER HWY
3rd row2020 S MUSKOGEE AVE
4th row3010 W GRANT LINE RD
5th row406 S WALTON BLVD
ValueCountFrequency (%)
rd 994
 
5.0%
st 790
 
4.0%
ave 619
 
3.1%
dr 571
 
2.9%
blvd 569
 
2.9%
s 533
 
2.7%
w 506
 
2.6%
n 500
 
2.5%
e 468
 
2.4%
highway 357
 
1.8%
Other values (5682) 13788
70.0%
2023-10-22T12:06:16.211153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14445
 
15.6%
E 5556
 
6.0%
R 4977
 
5.4%
A 4865
 
5.2%
0 4542
 
4.9%
1 4176
 
4.5%
S 3806
 
4.1%
N 3742
 
4.0%
T 3673
 
4.0%
D 3450
 
3.7%
Other values (42) 39512
42.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 56026
60.4%
Decimal Number 22138
 
23.9%
Space Separator 14445
 
15.6%
Other Punctuation 101
 
0.1%
Dash Punctuation 24
 
< 0.1%
Lowercase Letter 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 5556
 
9.9%
R 4977
 
8.9%
A 4865
 
8.7%
S 3806
 
6.8%
N 3742
 
6.7%
T 3673
 
6.6%
D 3450
 
6.2%
L 3357
 
6.0%
O 2951
 
5.3%
I 2727
 
4.9%
Other values (17) 16922
30.2%
Decimal Number
ValueCountFrequency (%)
0 4542
20.5%
1 4176
18.9%
5 2647
12.0%
2 2531
11.4%
3 1919
8.7%
4 1592
 
7.2%
7 1275
 
5.8%
6 1239
 
5.6%
9 1115
 
5.0%
8 1102
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
y 2
20.0%
d 2
20.0%
k 1
10.0%
w 1
10.0%
c 1
10.0%
a 1
10.0%
m 1
10.0%
e 1
10.0%
Other Punctuation
ValueCountFrequency (%)
. 86
85.1%
# 6
 
5.9%
, 4
 
4.0%
/ 3
 
3.0%
' 2
 
2.0%
Space Separator
ValueCountFrequency (%)
14445
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56036
60.4%
Common 36708
39.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 5556
 
9.9%
R 4977
 
8.9%
A 4865
 
8.7%
S 3806
 
6.8%
N 3742
 
6.7%
T 3673
 
6.6%
D 3450
 
6.2%
L 3357
 
6.0%
O 2951
 
5.3%
I 2727
 
4.9%
Other values (25) 16932
30.2%
Common
ValueCountFrequency (%)
14445
39.4%
0 4542
 
12.4%
1 4176
 
11.4%
5 2647
 
7.2%
2 2531
 
6.9%
3 1919
 
5.2%
4 1592
 
4.3%
7 1275
 
3.5%
6 1239
 
3.4%
9 1115
 
3.0%
Other values (7) 1227
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92743
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14445
 
15.6%
E 5556
 
6.0%
R 4977
 
5.4%
A 4865
 
5.2%
0 4542
 
4.9%
1 4176
 
4.5%
S 3806
 
4.1%
N 3742
 
4.0%
T 3673
 
4.0%
D 3450
 
3.7%
Other values (41) 39511
42.6%
None
ValueCountFrequency (%)
Ñ 1
100.0%
Distinct2682
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:16.681839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.8057143
Min length3

Characters and Unicode

Total characters46230
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1784 ?
Unique (%)34.0%

Sample

1st rowROGERS
2nd rowTUNKHANNOCK
3rd rowTAHLEQUAH
4th rowTRACY
5th rowBENTONVILLE
ValueCountFrequency (%)
city 155
 
2.4%
san 68
 
1.0%
beach 60
 
0.9%
springs 59
 
0.9%
fort 52
 
0.8%
lake 45
 
0.7%
north 44
 
0.7%
west 42
 
0.6%
park 38
 
0.6%
las 37
 
0.6%
Other values (2538) 5986
90.9%
2023-10-22T12:06:17.535941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4441
 
9.6%
E 4414
 
9.5%
O 3767
 
8.1%
N 3686
 
8.0%
L 3677
 
8.0%
R 3211
 
6.9%
I 2926
 
6.3%
S 2664
 
5.8%
T 2533
 
5.5%
C 1562
 
3.4%
Other values (19) 13349
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 44892
97.1%
Space Separator 1336
 
2.9%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4441
 
9.9%
E 4414
 
9.8%
O 3767
 
8.4%
N 3686
 
8.2%
L 3677
 
8.2%
R 3211
 
7.2%
I 2926
 
6.5%
S 2664
 
5.9%
T 2533
 
5.6%
C 1562
 
3.5%
Other values (16) 12011
26.8%
Space Separator
ValueCountFrequency (%)
1336
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44892
97.1%
Common 1338
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4441
 
9.9%
E 4414
 
9.8%
O 3767
 
8.4%
N 3686
 
8.2%
L 3677
 
8.2%
R 3211
 
7.2%
I 2926
 
6.5%
S 2664
 
5.9%
T 2533
 
5.6%
C 1562
 
3.5%
Other values (16) 12011
26.8%
Common
ValueCountFrequency (%)
1336
99.9%
- 1
 
0.1%
. 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4441
 
9.6%
E 4414
 
9.5%
O 3767
 
8.1%
N 3686
 
8.0%
L 3677
 
8.0%
R 3211
 
6.9%
I 2926
 
6.3%
S 2664
 
5.8%
T 2533
 
5.5%
C 1562
 
3.4%
Other values (19) 13349
28.9%
Distinct1236
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:18.059658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length28
Median length25
Mean length7.3194286
Min length3

Characters and Unicode

Total characters38427
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique574 ?
Unique (%)10.9%

Sample

1st rowBENTON
2nd rowWYOMING
3rd rowCHEROKEE
4th rowSAN JOAQUIN
5th rowBENTON
ValueCountFrequency (%)
st 82
 
1.4%
jefferson 74
 
1.3%
maricopa 70
 
1.2%
orange 68
 
1.2%
san 67
 
1.1%
harris 60
 
1.0%
washington 57
 
1.0%
dallas 56
 
1.0%
tarrant 53
 
0.9%
city 53
 
0.9%
Other values (1251) 5195
89.0%
2023-10-22T12:06:18.694116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 4423
11.5%
E 3635
 
9.5%
O 3200
 
8.3%
N 3146
 
8.2%
R 2859
 
7.4%
L 2621
 
6.8%
S 2288
 
6.0%
I 2094
 
5.4%
T 1731
 
4.5%
C 1451
 
3.8%
Other values (31) 10979
28.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 37715
98.1%
Space Separator 586
 
1.5%
Other Punctuation 86
 
0.2%
Dash Punctuation 23
 
0.1%
Lowercase Letter 17
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4423
11.7%
E 3635
 
9.6%
O 3200
 
8.5%
N 3146
 
8.3%
R 2859
 
7.6%
L 2621
 
6.9%
S 2288
 
6.1%
I 2094
 
5.6%
T 1731
 
4.6%
C 1451
 
3.8%
Other values (16) 10267
27.2%
Lowercase Letter
ValueCountFrequency (%)
r 2
11.8%
n 2
11.8%
e 2
11.8%
w 2
11.8%
a 2
11.8%
l 2
11.8%
i 1
5.9%
t 1
5.9%
c 1
5.9%
s 1
5.9%
Other Punctuation
ValueCountFrequency (%)
. 82
95.3%
' 4
 
4.7%
Space Separator
ValueCountFrequency (%)
586
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37732
98.2%
Common 695
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4423
11.7%
E 3635
 
9.6%
O 3200
 
8.5%
N 3146
 
8.3%
R 2859
 
7.6%
L 2621
 
6.9%
S 2288
 
6.1%
I 2094
 
5.5%
T 1731
 
4.6%
C 1451
 
3.8%
Other values (27) 10284
27.3%
Common
ValueCountFrequency (%)
586
84.3%
. 82
 
11.8%
- 23
 
3.3%
' 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4423
11.5%
E 3635
 
9.5%
O 3200
 
8.3%
N 3146
 
8.2%
R 2859
 
7.4%
L 2621
 
6.8%
S 2288
 
6.0%
I 2094
 
5.4%
T 1731
 
4.5%
C 1451
 
3.8%
Other values (31) 10979
28.6%
Distinct52
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:18.964698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10500
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAR
2nd rowPA
3rd rowOK
4th rowCA
5th rowAR
ValueCountFrequency (%)
tx 597
 
11.4%
fl 389
 
7.4%
ca 310
 
5.9%
nc 214
 
4.1%
ga 212
 
4.0%
il 185
 
3.5%
oh 171
 
3.3%
pa 159
 
3.0%
mo 156
 
3.0%
tn 150
 
2.9%
Other values (42) 2707
51.6%
2023-10-22T12:06:19.357025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1557
14.8%
N 946
 
9.0%
T 861
 
8.2%
L 856
 
8.2%
C 787
 
7.5%
I 643
 
6.1%
M 638
 
6.1%
O 611
 
5.8%
X 597
 
5.7%
F 389
 
3.7%
Other values (14) 2615
24.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1557
14.8%
N 946
 
9.0%
T 861
 
8.2%
L 856
 
8.2%
C 787
 
7.5%
I 643
 
6.1%
M 638
 
6.1%
O 611
 
5.8%
X 597
 
5.7%
F 389
 
3.7%
Other values (14) 2615
24.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 10500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1557
14.8%
N 946
 
9.0%
T 861
 
8.2%
L 856
 
8.2%
C 787
 
7.5%
I 643
 
6.1%
M 638
 
6.1%
O 611
 
5.8%
X 597
 
5.7%
F 389
 
3.7%
Other values (14) 2615
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1557
14.8%
N 946
 
9.0%
T 861
 
8.2%
L 856
 
8.2%
C 787
 
7.5%
I 643
 
6.1%
M 638
 
6.1%
O 611
 
5.8%
X 597
 
5.7%
F 389
 
3.7%
Other values (14) 2615
24.9%
Distinct5157
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:19.682094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters52500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5064 ?
Unique (%)96.5%

Sample

1st row72756-3246
2nd row18657-8071
3rd row74464-5439
4th row95304-9402
5th row72712-5705
ValueCountFrequency (%)
57106-0704 2
 
< 0.1%
28083-6426 2
 
< 0.1%
76542-5201 2
 
< 0.1%
44011-1069 2
 
< 0.1%
21237-3034 2
 
< 0.1%
00659-0000 2
 
< 0.1%
15301-2974 2
 
< 0.1%
70345-4053 2
 
< 0.1%
71111-0000 2
 
< 0.1%
30165-1913 2
 
< 0.1%
Other values (5147) 5230
99.6%
2023-10-22T12:06:20.204460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6925
13.2%
1 5370
10.2%
3 5342
10.2%
2 5294
10.1%
- 5250
10.0%
4 4633
8.8%
5 4430
8.4%
7 4397
8.4%
6 4110
7.8%
8 3607
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47250
90.0%
Dash Punctuation 5250
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6925
14.7%
1 5370
11.4%
3 5342
11.3%
2 5294
11.2%
4 4633
9.8%
5 4430
9.4%
7 4397
9.3%
6 4110
8.7%
8 3607
7.6%
9 3142
6.6%
Dash Punctuation
ValueCountFrequency (%)
- 5250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6925
13.2%
1 5370
10.2%
3 5342
10.2%
2 5294
10.1%
- 5250
10.0%
4 4633
8.8%
5 4430
8.4%
7 4397
8.4%
6 4110
7.8%
8 3607
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6925
13.2%
1 5370
10.2%
3 5342
10.2%
2 5294
10.1%
- 5250
10.0%
4 4633
8.8%
5 4430
8.4%
7 4397
8.4%
6 4110
7.8%
8 3607
6.9%

businessunit_status_code
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4851429
Minimum0
Maximum6
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:20.387080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q35
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5085545
Coefficient of variation (CV)0.43285298
Kurtosis-1.9687251
Mean3.4851429
Median Absolute Deviation (MAD)0
Skewness0.022313369
Sum18297
Variance2.2757365
MonotonicityNot monotonic
2023-10-22T12:06:20.555650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 2649
50.5%
5 2572
49.0%
6 22
 
0.4%
0 5
 
0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 5
 
0.1%
2 2649
50.5%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 2572
49.0%
6 22
 
0.4%
ValueCountFrequency (%)
6 22
 
0.4%
5 2572
49.0%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 2649
50.5%
0 5
 
0.1%

op_status
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
Open
5247 
Closed
 
3

Length

Max length6
Median length4
Mean length4.0011429
Min length4

Characters and Unicode

Total characters21006
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen
2nd rowOpen
3rd rowOpen
4th rowOpen
5th rowOpen

Common Values

ValueCountFrequency (%)
Open 5247
99.9%
Closed 3
 
0.1%

Length

2023-10-22T12:06:20.770197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:20.934608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
open 5247
99.9%
closed 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 5250
25.0%
O 5247
25.0%
p 5247
25.0%
n 5247
25.0%
C 3
 
< 0.1%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15756
75.0%
Uppercase Letter 5250
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5250
33.3%
p 5247
33.3%
n 5247
33.3%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
O 5247
99.9%
C 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 21006
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5250
25.0%
O 5247
25.0%
p 5247
25.0%
n 5247
25.0%
C 3
 
< 0.1%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5250
25.0%
O 5247
25.0%
p 5247
25.0%
n 5247
25.0%
C 3
 
< 0.1%
l 3
 
< 0.1%
o 3
 
< 0.1%
s 3
 
< 0.1%
d 3
 
< 0.1%

Modified_Operating_Hours_
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing5247
Missing (%)99.9%
Memory size10.4 KiB
False
 
3
(Missing)
5247 
ValueCountFrequency (%)
False 3
 
0.1%
(Missing) 5247
99.9%
2023-10-22T12:06:21.060069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Pharmacy_open_to_public_
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
True
5249 
False
 
1
ValueCountFrequency (%)
True 5249
> 99.9%
False 1
 
< 0.1%
2023-10-22T12:06:21.184405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
True
5250 
ValueCountFrequency (%)
True 5250
100.0%
2023-10-22T12:06:21.421009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

bu_num
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3184.756
Minimum1
Maximum9894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2023-10-22T12:06:21.605452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile278.45
Q11371.25
median2810.5
Q34925.75
95-th percentile6955.75
Maximum9894
Range9893
Interquartile range (IQR)3554.5

Descriptive statistics

Standard deviation2159.4932
Coefficient of variation (CV)0.6780718
Kurtosis-0.73966895
Mean3184.756
Median Absolute Deviation (MAD)1694
Skewness0.47704628
Sum16719969
Variance4663411
MonotonicityNot monotonic
2023-10-22T12:06:21.932595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5894 1
 
< 0.1%
5891 1
 
< 0.1%
5890 1
 
< 0.1%
589 1
 
< 0.1%
5889 1
 
< 0.1%
5888 1
 
< 0.1%
5886 1
 
< 0.1%
5884 1
 
< 0.1%
5883 1
 
< 0.1%
Other values (5240) 5240
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
9894 1
< 0.1%
8958 1
< 0.1%
8930 1
< 0.1%
8331 1
< 0.1%
8299 1
< 0.1%
8298 1
< 0.1%
8297 1
< 0.1%
8296 1
< 0.1%
8295 1
< 0.1%
8294 1
< 0.1%

Grocery_delivery_service
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing435
Missing (%)8.3%
Memory size10.4 KiB
False
3157 
True
1658 
(Missing)
435 
ValueCountFrequency (%)
False 3157
60.1%
True 1658
31.6%
(Missing) 435
 
8.3%
2023-10-22T12:06:22.190085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Grocery_delivery_status
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing2385
Missing (%)45.4%
Memory size41.1 KiB
Available
2346 
Not Available
519 

Length

Max length13
Median length9
Mean length9.7246073
Min length9

Characters and Unicode

Total characters27861
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAvailable
2nd rowNot Available
3rd rowNot Available
4th rowAvailable
5th rowAvailable

Common Values

ValueCountFrequency (%)
Available 2346
44.7%
Not Available 519
 
9.9%
(Missing) 2385
45.4%

Length

2023-10-22T12:06:22.440909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:22.625605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
available 2865
84.7%
not 519
 
15.3%

Most occurring characters

ValueCountFrequency (%)
a 5730
20.6%
l 5730
20.6%
A 2865
10.3%
v 2865
10.3%
i 2865
10.3%
b 2865
10.3%
e 2865
10.3%
N 519
 
1.9%
o 519
 
1.9%
t 519
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23958
86.0%
Uppercase Letter 3384
 
12.1%
Space Separator 519
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5730
23.9%
l 5730
23.9%
v 2865
12.0%
i 2865
12.0%
b 2865
12.0%
e 2865
12.0%
o 519
 
2.2%
t 519
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A 2865
84.7%
N 519
 
15.3%
Space Separator
ValueCountFrequency (%)
519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27342
98.1%
Common 519
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5730
21.0%
l 5730
21.0%
A 2865
10.5%
v 2865
10.5%
i 2865
10.5%
b 2865
10.5%
e 2865
10.5%
N 519
 
1.9%
o 519
 
1.9%
t 519
 
1.9%
Common
ValueCountFrequency (%)
519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5730
20.6%
l 5730
20.6%
A 2865
10.3%
v 2865
10.3%
i 2865
10.3%
b 2865
10.3%
e 2865
10.3%
N 519
 
1.9%
o 519
 
1.9%
t 519
 
1.9%

Online_grocery_pickup
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing106
Missing (%)2.0%
Memory size10.4 KiB
True
3616 
False
1528 
(Missing)
 
106
ValueCountFrequency (%)
True 3616
68.9%
False 1528
29.1%
(Missing) 106
 
2.0%
2023-10-22T12:06:22.773254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Online_grocery_pickup_status
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.1%
Missing1473
Missing (%)28.1%
Memory size41.1 KiB
Available
3144 
Not Available
633 

Length

Max length13
Median length9
Mean length9.6703733
Min length9

Characters and Unicode

Total characters36525
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Available
2nd rowAvailable
3rd rowAvailable
4th rowNot Available
5th rowNot Available

Common Values

ValueCountFrequency (%)
Available 3144
59.9%
Not Available 633
 
12.1%
(Missing) 1473
28.1%

Length

2023-10-22T12:06:22.967405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T12:06:23.144945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
available 3777
85.6%
not 633
 
14.4%

Most occurring characters

ValueCountFrequency (%)
a 7554
20.7%
l 7554
20.7%
A 3777
10.3%
v 3777
10.3%
i 3777
10.3%
b 3777
10.3%
e 3777
10.3%
N 633
 
1.7%
o 633
 
1.7%
t 633
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31482
86.2%
Uppercase Letter 4410
 
12.1%
Space Separator 633
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7554
24.0%
l 7554
24.0%
v 3777
12.0%
i 3777
12.0%
b 3777
12.0%
e 3777
12.0%
o 633
 
2.0%
t 633
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
A 3777
85.6%
N 633
 
14.4%
Space Separator
ValueCountFrequency (%)
633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35892
98.3%
Common 633
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7554
21.0%
l 7554
21.0%
A 3777
10.5%
v 3777
10.5%
i 3777
10.5%
b 3777
10.5%
e 3777
10.5%
N 633
 
1.8%
o 633
 
1.8%
t 633
 
1.8%
Common
ValueCountFrequency (%)
633
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7554
20.7%
l 7554
20.7%
A 3777
10.3%
v 3777
10.3%
i 3777
10.3%
b 3777
10.3%
e 3777
10.3%
N 633
 
1.7%
o 633
 
1.7%
t 633
 
1.7%

Interactions

2023-10-22T12:06:06.279179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:00.835995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:01.741561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:02.725030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:03.934556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:05.239789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:06.442185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:01.062877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:01.982615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:02.868789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:04.114491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:05.489843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:06.579178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:01.188826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:02.121608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:03.083159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:04.357408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:05.641386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:06.798169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:01.338854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:02.290308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:03.273620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:04.617912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:05.809559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:07.081791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:01.470109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:02.440706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:03.454928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:04.794655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:05.957971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:07.363012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:01.609940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:02.587042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:03.731506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:04.995911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-22T12:06:06.122936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-22T12:06:23.290954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
XYobjectidbusinessunit_numberbusinessunit_status_codebu_numbusinessunit_banner_descriptionbusinessunit_type_descriptionbusinessunit_isstoreopenop_statusPharmacy_open_to_public_Grocery_delivery_serviceGrocery_delivery_statusOnline_grocery_pickupOnline_grocery_pickup_status
X1.0000.087-0.0530.0230.0120.0230.1360.0400.0790.0000.0000.0670.0820.0300.058
Y0.0871.000-0.1810.0100.0940.0100.1020.0350.0700.0000.0000.0710.0000.0210.018
objectid-0.053-0.1811.0000.474-0.2000.4740.2210.5000.0240.0410.0000.0590.0000.0370.000
businessunit_number0.0230.0100.4741.000-0.5521.0000.4110.7890.0420.0000.0000.1240.0000.0740.023
businessunit_status_code0.0120.094-0.200-0.5521.000-0.5520.2280.0640.9770.0000.0000.0430.0000.0760.000
bu_num0.0230.0100.4741.000-0.5521.0000.4110.7890.0420.0000.0000.1240.0000.0740.023
businessunit_banner_description0.1360.1020.2210.4110.2280.4111.0000.9990.2180.0000.0000.2000.0480.1720.044
businessunit_type_description0.0400.0350.5000.7890.0640.7890.9991.0000.0000.0000.0000.1870.0000.1050.009
businessunit_isstoreopen0.0790.0700.0240.0420.9770.0420.2180.0001.0000.0000.0000.0000.0000.0110.000
op_status0.0000.0000.0410.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
Pharmacy_open_to_public_0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.000
Grocery_delivery_service0.0670.0710.0590.1240.0430.1240.2000.1870.0000.0000.0001.0000.3310.4220.249
Grocery_delivery_status0.0820.0000.0000.0000.0000.0000.0480.0000.0000.0001.0000.3311.0000.2360.918
Online_grocery_pickup0.0300.0210.0370.0740.0760.0740.1720.1050.0110.0000.0000.4220.2361.0000.175
Online_grocery_pickup_status0.0580.0180.0000.0230.0000.0230.0440.0090.0000.0001.0000.2490.9180.1751.000

Missing values

2023-10-22T12:06:07.792760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T12:06:08.500860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-22T12:06:09.052778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

XYobjectidbusinessunit_namebusinessunit_numberbusinessunit_banner_descriptionbusinessunit_type_descriptionbusinessunit_isstoreopenfacilitydetails_location_locatifacilitydetails_location_loca_1facilitydetails_location_loca_2facilitydetails_location_loca_3facilitydetails_location_loca_8businessunit_status_codeop_statusModified_Operating_Hours_Pharmacy_open_to_public_Fuel_Station_open_to_public_bu_numGrocery_delivery_serviceGrocery_delivery_statusOnline_grocery_pickupOnline_grocery_pickup_status
0-94.14905436.33130029040ROGERS, AR1WM SupercenterRetailTrue2110 W WALNUT STROGERSBENTONAR72756-32465OpenNaNYY1YesNaNYesNot Available
1-75.94744141.52398129041TUNKHANNOCK, PA2024WM SupercenterRetailTrue808 HUNTER HWYTUNKHANNOCKWYOMINGPA18657-80715OpenNaNYY2024NoAvailableNoAvailable
2-94.97981435.88880729042TAHLEQUAH OK10WM SupercenterRetailTrue2020 S MUSKOGEE AVETAHLEQUAHCHEROKEEOK74464-54395OpenNaNYY10NoNaNNoAvailable
3-121.47171537.75165929043TRACY, CA2025WM SupercenterRetailTrue3010 W GRANT LINE RDTRACYSAN JOAQUINCA95304-94025OpenNaNYY2025NoNot AvailableNoNot Available
4-94.22484436.36817329044BENTONVILLE, AR100WM SupercenterRetailTrue406 S WALTON BLVDBENTONVILLEBENTONAR72712-57055OpenNaNYY100NoNot AvailableNoNot Available
5-66.64257817.99736029045PONCE-PR2026WM SupercenterRetailTrue3305 AVE.BARAMAYA SUITE 100PONCEPONCEPR00728-00005OpenNaNYY2026NaNNaNYesNaN
6-97.47700325.92514229046BROWNSVILLE TX1000WM SupercenterRetailTrue2721 BOCA CHICA BLVDBROWNSVILLECAMERONTX78521-35015OpenNaNYY1000YesNaNYesAvailable
7-78.83505239.62490329047LAVALE, MD2027WM SupercenterRetailTrue12500 COUNTRY CLUB MALL RDLAVALEALLEGANYMD21502-75535OpenNaNYY2027YesAvailableYesAvailable
8-104.66489838.23231629048PUEBLO, CO1001WM SupercenterRetailTrue4080 W NORTHERN AVEPUEBLOPUEBLOCO81005-35035OpenNaNYY1001YesAvailableYesAvailable
9-117.45533933.93707129049RIVERSIDE (S), CA2028WM SupercenterRetailTrue5200 VAN BUREN BLVDRIVERSIDERIVERSIDECA92503-25445OpenNaNYY2028NoNaNYesNaN
XYobjectidbusinessunit_namebusinessunit_numberbusinessunit_banner_descriptionbusinessunit_type_descriptionbusinessunit_isstoreopenfacilitydetails_location_locatifacilitydetails_location_loca_1facilitydetails_location_loca_2facilitydetails_location_loca_3facilitydetails_location_loca_8businessunit_status_codeop_statusModified_Operating_Hours_Pharmacy_open_to_public_Fuel_Station_open_to_public_bu_numGrocery_delivery_serviceGrocery_delivery_statusOnline_grocery_pickupOnline_grocery_pickup_status
5240-82.45571829.06330936688DUNNELLON FL960WM SupercenterRetailTrue11012 NO. WILLIAMS STDUNNELLONMARIONFL34432-83105OpenNaNYY960YesAvailableYesAvailable
5241-90.49828530.07471636690LAPLACE LA961WM SupercenterRetailTrue1616 W AIRLINE HWYLA PLACEST. JOHN THE BAPTIST PARISHLA70068-33315OpenNaNYY961NoAvailableYesAvailable
5242-104.52327537.13872836693TRINIDAD CO962WM SupercenterRetailTrue2921 TOUPAL DRTRINIDADLAS ANIMASCO81082-87405OpenNaNYY962YesAvailableYesAvailable
5243-97.79486232.73149336695WEATHERFORD TX963WM SupercenterRetailTrue1836 S MAIN STWEATHERFORDPARKERTX76086-55065OpenNaNYY963NoAvailableYesAvailable
5244-106.31159631.68289836698EL PASO (S) TX964WM SupercenterRetailTrue9441 ALAMEDA AVEEL PASOEL PASOTX79907-56015OpenNaNYY964YesAvailableYesAvailable
5245-90.50857744.02102436700TOMAH WI965WM SupercenterRetailTrue222 W MCCOY BLVDTOMAHMONROEWI54660-32915OpenNaNYY965NoNaNYesNaN
5246-108.56199437.34656836702CORTEZ CO966WM SupercenterRetailTrue1835 E MAIN STCORTEZMONTEZUMACO81321-30375OpenNaNYY966YesNaNYesNaN
5247-82.63121828.45839736704SPRINGHILL / BROOKSVILLE967WM SupercenterRetailTrue1485 COMMERCIAL WAYSPRING HILLHERNANDOFL34606-45255OpenNaNYY967YesAvailableYesAvailable
5248-81.72225128.00665136705WINTER HAVEN FL968WM SupercenterRetailTrue355 CYPRESS GARDENS BLVDWINTER HAVENPOLKFL33880-44525OpenNaNYY968YesAvailableYesAvailable
5249-89.08869730.42322436706GULFPORT MS969WM SupercenterRetailTrue9350 HIGHWAY 49GULFPORTHARRISONMS39503-42135OpenNaNYY969NoNot AvailableYesAvailable